January 11, 2020

Applications of chromosome conformation technologies

Day 2

Biological insights

Several motivations to investigate chromosome conformation:

  • Mechanics of regulation: Promoter-enhancer, DNA repair, insulation, …
  • Fundamental physics of chromatin: phase separation, compaction, …
  • 3D model of chromosomes, their organization and segregation

What to look for in a contact map ?

Extract signals for more quantitative analyses:

  • Contact probability vs genomic distance
  • Insulation score
  • Compartment eigenvectors
  • Feature detection: loops, domains, hairpins, …

Distance-dependent contacts

The diagonal of Hi-C map contains useful information:

  • Distant-dependent contact decay follows a power-law

  • The slope of the curve gives compaction information

  • Can be used to compare polymer models

    Adapted from Muller et al., 2018

Insulating boundaries

Chromatin interaction domains are important for gene regulation:

  • form compact neighbourhoods of co-regulated genes
  • Domain borders prevent interactions with elements outside
  • TAD boundary disruption results in gene deregulation (Lupianez et al., 2015)

Insulation score

Insulation: Contact depletion between domains

Insulation can be quantified with a numeric score.

Chen et al., 2018

Chromatin compartments

Active and inactive chromatin is usually classified into A/B compartments

In Hi-C those compartments appear as a plaid-like pattern.

Dataset 4DNESYTUBW2E from Oksuz, Yang et al., bioRxiv 2020.

Compartments eigenvector

  • Most common method to identify compartments: PCA on the Hi-C matrix
  • Eigenvectors explaining the most variance will contain compartments.
  • Must be validated with an external correlated signal (e.g. GC%)

Compartments eigenvector

Feature detection

Often we want to automatically find where changes are happening in the genome, such as:

  • Chromatin loops
  • Domains (CID, TADs, …)
  • General contact intensity changes

Adapted from Rao et al., 2014"

Limitations of Hi-C

Despite all its uses, Hi-C has several limitations to gain biological insights:

  • Resolution limited by restriction enzyme and coverage
  • Limited to 2-ways interactions. (methods in development)
  • No absolute quantifications, counts are relative

Alternative uses of Hi-C

Baudry et al., 2019

Alternative uses of Hi-C

  • Structural variant detection (deletions, inversions, …)

Alternative uses of Hi-C

  • Resolving species from metagenomic assemblies:
    • MetaTOR: Metagenome binning using Hi-C.
    • Strain3C: Strain-level genomes resolution using Hi-C.
Marbouty et al., 2014

Marbouty et al., 2014

Alternative uses of Hi-C

  • Phasing haplotypes: Stronger cis- than trans-contacts

Exercises

  • Use python scripting to interact with Hi-C data
  • Extract quantitative signal from contact maps
  • Feature detection on Hi-C